aiqbench analysis
This paper introduces What If TSF (WIT), a new benchmark designed to evaluate how multimodal time series models respond to varying future scenarios beyond simple predictive accuracy. By distinguishing between factual utility, relational logic, and empirical plausibility, the study reveals that high accuracy does not guarantee appropriate scenario handling in LLM-driven forecasting systems. This research is critical for advancing the reliability of AI agents operating in complex, information-rich environments where understanding causal narratives matters as much as numerical precision.
What If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecasting
arXiv:2601.08509v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multimodal settings. Such information can improve forecasting performance, but accuracy alone may not reveal whether models appropriately respond to it: a model may ignore relevant information, fail to distinguish scenarios with different implications, or overreact to irrelevant or weak signals. We introduce What If TSF (WIT), a benchmark for evaluating whether models effectively respond to future scenarios. WIT constructs controlled scenario sets by fixing the forecasting state while systematically varying future scenarios, enabling three complementary evaluations: Factual Comparison, which measures the predictive utility of factual scenario information; Relational Comparison, which evaluates whether forecasts satisfy expected direction, contrast, intensity, and restraint relations across scenarios; and Grounded Comparison, which assesses whether scenario-induced revision trajectories are empirically plausible relative to comparable real cases. Experiments show that factual accuracy gains do not consistently translate into appropriate responses to alternative scenarios, demonstrating the need to evaluate multimodal forecasting beyond predictive accuracy.
Abstract:Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multimodal settings. Such information can improve forecasting performance, but accuracy alone may not reveal whether models appropriately respond to it: a model may ignore relevant information, fail to distinguish scenarios with different implications, or overreact to irrelevant or weak signals. We introduce What If TSF (WIT), a benchmark for evaluating whether models effectively respond to future scenarios. WIT constructs controlled scenario sets by fixing the forecasting state while systematically varying future scenarios, enabling three complementary evaluations: Factual Comparison, which measures the predictive utility of factual scenario information; Relational Comparison, which evaluates whether forecasts satisfy expected direction, contrast, intensity, and restraint relations across scenarios; and Grounded Comparison, which assesses whether scenario-induced revision trajectories are empirically plausible relative to comparable real cases. Experiments show that factual accuracy gains do not consistently translate into appropriate responses to alternative scenarios, demonstrating the need to evaluate multimodal forecasting beyond predictive accuracy.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2601.08509 [cs.AI]
(or arXiv:2601.08509v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2601.08509 Focus to learn more arXiv-issued DOI via DataCite
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